Smart Fraud Detection and Prevention: Leveraging Generative AI for Enhanced Payment Security

Abstract

The extended digitalization of financial transactions has posed exceptional problems to fraud detection and prevention systems, prompting the development from conventional rule-based structures to superior artificial intelligence systems. Smart fraud detection structures based on generative AI are a revolutionary step towards countering swiftly evolving and complicated fraud schemes that take advantage of weaknesses in virtual fee structures. These cognizant systems showcase better overall performance in managing high transaction volumes, detecting latent styles, and dynamically adjusting themselves to new danger vectors without the need for human intervention or significant reconfigurations. The combination of generative AI with standard machine learning techniques allows for more advanced anomaly detection, contextualization, and real-time response against threats, which excel far beyond traditional methods of detection. Modern implementations exhibit significant gains in detection accuracy, accompanied by the lowering of false positive rates, thus improving operational effectiveness and customer satisfaction. The hybrid human-AI collaboration mode takes advantage of computational capabilities and pattern recognition of artificial intelligence while maintaining critical human expertise for context-based decision-making and strategic control. Environmental, economic, and social implications are not limited to direct fraud avoidance but cover wider financial ecosystem stability, energy efficiency from optimized computational power, and a greater level of financial inclusion for vulnerable groups through secure digital payment access.